Method and apparatus for an enhanced data pipeline

ABSTRACT

Aspects of the subject disclosure may include, for example, monitoring a plurality of data packets in a network by analyzing header information to detect an indicator associated with a first data object type, identifying a first data object in a first set of data packets according to the monitoring and according to a model relating to the first data object type, instantiating an intelligent router at the network, switching the first set of data packets to the intelligent router to cause the intelligent router to generate an extracted first data object from the first set of data packets, transmitting the extracted first data object to a client device via the data pipeline of the network responsive to a request from the client device for the first data object, and decommissioning the intelligent router after the transmitting the extracted first data object. Other embodiments are disclosed.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation of and claims priority to U.S.application Ser. No. 16/511,654, filed Jul. 15, 2019. The contents ofthe foregoing are hereby incorporated by reference into this applicationas if set forth herein in full.

FIELD OF THE DISCLOSURE

The subject disclosure relates to a method and apparatus for media datapipeline.

BACKGROUND

Modern telecommunications systems provide consumers with telephonycapabilities while accessing a large variety of content. Consumers areno longer bound to specific locations when communicating with others orwhen enjoying multimedia content or accessing the varied resourcesavailable via the Internet. Network capabilities have expanded and havecreated additional interconnections and new opportunities for usingmobile communication devices in a variety of situations. Intelligentdevices offer new means for experiencing network interactions in waysthat anticipate consumer desires and provide solutions to problems.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale, and wherein:

FIG. 1 is a block diagram illustrating an exemplary, non-limitingembodiment of a communications network in accordance with variousaspects described herein.

FIG. 2A-2C is a block diagram illustrating an example, non-limitingembodiment of a system functioning within the communication network ofFIG. 1 in accordance with various aspects described herein.

FIG. 2D depicts an illustrative embodiment of a method in accordancewith various aspects described herein.

FIG. 3 is a block diagram illustrating an example, non-limitingembodiment of a virtualized communication network in accordance withvarious aspects described herein.

FIG. 4 is a block diagram of an example, non-limiting embodiment of acomputing environment in accordance with various aspects describedherein.

FIG. 5 is a block diagram of an example, non-limiting embodiment of amobile network platform in accordance with various aspects describedherein.

FIG. 6 is a block diagram of an example, non-limiting embodiment of acommunication device in accordance with various aspects describedherein.

DETAILED DESCRIPTION

The subject disclosure describes, among other things, illustrativeembodiments for processing data in a data pipeline. In one or moreembodiments, a system can monitor a data pipeline to identify dataobjects in data packets carried by the data pipeline. The system caninstantiate intelligent routers at the data pipeline, including servicessuch as solid-state memory devices, graphical processing units, and/orpacket monitor functions. The system can switch data packets associatedwith the data objects to the intelligent routers, where the data objectsare extracted from the data packets and stored in cache memory. Uponreceiving requests from client devices, the intelligent routers cantransmit the extracted data objects from the cache memory to the clientdevices. The intelligent routers can, in turn, be decommissionedsubsequent to transmission of the extracted data objects.

One or more aspects of the subject disclosure include a device,including a processing system including a processor, and a memory thatstores executable instructions that, when executed by the processingsystem, facilitate performance of operations. The operations can includemonitoring a plurality of data packets in a data pipeline of a networkand, in turn, identifying a first data object in the plurality of datapackets according to the monitoring of the plurality of data packets,where the monitoring can include analyzing header information associatedwith the plurality of data packets. The operations can also includeinstantiating an intelligent router at the data pipeline of the networkresponsive to the identifying the first data object, where theintelligent router can include a solid-state memory device, a graphicalprocessing unit, and packet monitoring functions. The operations canfurther include switching a first set of data packets of the pluralityof data packets associated with the first data object to the intelligentrouter for extracting the first data object from the first set of datapackets to generate an extracted first data object responsive to theidentifying the first data object in the plurality of data packets.Operation of the intelligent router can be directed according toextended tables associated with a domain-specific programming language.For example, the P4 programming language is specific to the domain ofrouting and the control of routing operations. The operations caninclude receiving a request from a client device for the first dataobject and, in turn, transmitting the extracted first data object to theclient device via the data pipeline of the network responsive to therequest for the first data object. The operations can also includedecommissioning the intelligent router responsive to the transmittingthe extracted first data object.

One or more aspects of the subject disclosure include a machine-readablemedium, comprising executable instructions that, when executed by aprocessing system including a processor, facilitate performance ofoperations. The operations can include monitoring a plurality of datapackets in a data pipeline of a network and identifying a first dataobject in the plurality of data packets according to the monitoring ofthe plurality of data packets. The identifying the first data object inthe plurality of data packets can further comprise identifying metadataassociated with the first data object. The monitoring can includeanalyzing header information associated with the plurality of datapackets. The operations can also include instantiating an intelligentrouter at the data pipeline of the network responsive to the identifyingthe first data object. The operations can further include switching afirst set of data packets of the plurality of data packets associatedwith the first data object to the intelligent router for extracting thefirst data object from the first set of data packets to generate anextracted first data object responsive to the identifying the first dataobject in the plurality of data packets. The operations can includetransmitting the extracted first data object to a client device via thedata pipeline of the network.

One or more aspects of the subject disclosure include a method,performing, by a processing system including a processor, stepsincluding monitoring a plurality of data packets in a data pipeline of anetwork, where the monitoring can include analyzing header informationassociated with the plurality of data packets. The method can includeidentifying a first data object in the plurality of data packetsaccording to the monitoring of the plurality of data packets, where theidentifying the first data object in the plurality of data packets canfurther comprise identifying metadata associated with the first dataobject. The method can also include switching a first set of datapackets of the plurality of data packets associated with the first dataobject to an intelligent router for extracting the first data objectfrom the first set of data packets to generate an extracted first dataobject responsive to the identifying the first data object in theplurality of data packets. The method can further include transmittingthe extracted first data object to a client device via the data pipelineof the network.

Referring now to FIG. 1, a block diagram is shown illustrating anexample, non-limiting embodiment of a communications network 100 inaccordance with various aspects described herein. For example,communications network 100 can facilitate in whole or in part processingdata in a data pipeline. A system can monitor a data pipeline toidentify data objects in data packets carried by the data pipeline. Thesystem can instantiate intelligent routers at the data pipeline,including services such as solid-state memory devices, graphicalprocessing units, and/or a packet monitoring functions. The system canswitch data packets associated with the data objects to the intelligentrouters, where the data objects are extracted from the data packets andstored in cache memory. Upon receiving requests from client devices, theintelligent routers can transmit the extracted data objects from thecache memory to the client devices. The intelligent routers can, inturn, be decommissioned subsequent to transmission of the extracted dataobjects.

In particular, a communications network 125 is presented for providingbroadband access 110 to a plurality of data terminals 114 via accessterminal 112, wireless access 120 to a plurality of mobile devices 124and vehicle 126 via base station or access point 122, voice access 130to a plurality of telephony devices 134, via switching device 132 and/ormedia access 140 to a plurality of audio/video display devices 144 viamedia terminal 142. In particular, a communications network 125 ispresented for providing broadband access 110 to a plurality of dataterminals 114 via access terminal 112, wireless access 120 to aplurality of mobile devices 124 and vehicle 126 via base station oraccess point 122, voice access 130 to a plurality of telephony devices134, via switching device 132 and/or media access 140 to a plurality ofaudio/video display devices 144 via media terminal 142. In addition,communication network 125 is coupled to one or more content sources 175of audio, video, graphics, text and/or other media. While broadbandaccess 110, wireless access 120, voice access 130 and media access 140are shown separately, one or more of these forms of access can becombined to provide multiple access services to a single client device(e.g., mobile devices 124 can receive media content via media terminal142, data terminal 114 can be provided voice access via switching device132, and so on).

The communications network 125 includes a plurality of network elements(NE) 150, 152, 154, 156, etc. for facilitating the broadband access 110,wireless access 120, voice access 130, media access 140 and/or thedistribution of content from content sources 175. The communicationsnetwork 125 can include a circuit switched or packet switched network, avoice over Internet protocol (VoIP) network, Internet protocol (IP)network, a cable network, a passive or active optical network, a 4G, 5G,or higher generation wireless access network, WIMAX network,UltraWideband network, personal area network or other wireless accessnetwork, a broadcast satellite network and/or other communicationsnetwork.

In various embodiments, the access terminal 112 can include a digitalsubscriber line access multiplexer (DSLAM), cable modem terminationsystem (CMTS), optical line terminal (OLT) and/or other access terminal.The data terminals 114 can include personal computers, laptop computers,netbook computers, tablets or other computing devices along with digitalsubscriber line (DSL) modems, data over coax service interfacespecification (DOCSIS) modems or other cable modems, a wireless modemsuch as a 4G, 5G, or higher generation modem, an optical modem and/orother access devices.

In various embodiments, the base station or access point 122 can includea 4G, 5G, or higher generation base station, an access point thatoperates via an 802.11 standard such as 802.11n, 802.11ac or otherwireless access terminal. The mobile devices 124 can include mobilephones, e-readers, tablets, phablets, wireless modems, and/or othermobile computing devices.

In various embodiments, the switching device 132 can include a privatebranch exchange or central office switch, a media services gateway, VoIPgateway or other gateway device and/or other switching device. Thetelephony devices 134 can include traditional telephones (with orwithout a terminal adapter), VoIP telephones and/or other telephonydevices.

In various embodiments, the media terminal 142 can include a cablehead-end or other TV head-end, a satellite receiver, gateway or othermedia terminal 142. The display devices 144 can include televisions withor without a set top box, personal computers and/or other displaydevices.

In various embodiments, the content sources 175 include broadcasttelevision and radio sources, video on demand platforms and streamingvideo and audio services platforms, one or more content data networks,data servers, web servers and other content servers, and/or othersources of media.

In various embodiments, the communications network 125 can includewired, optical and/or wireless links and the network elements 150, 152,154, 156, etc. can include service switching points, signal transferpoints, service control points, network gateways, media distributionhubs, servers, firewalls, routers, edge devices, switches and othernetwork nodes for routing and controlling communications traffic overwired, optical and wireless links as part of the Internet and otherpublic networks as well as one or more private networks, for managingsubscriber access, for billing and network management and for supportingother network functions.

FIGS. 2A-2C is a block diagram illustrating an example, non-limitingembodiment of a system functioning within the communication network ofFIG. 1 in accordance with various aspects described herein. FIG. 2Adepicts an illustrative embodiment of a system 200 for facilitating, inwhole or in part, processing data in a data pipeline. A system canmonitor a data pipeline to identify data objects in data packets carriedby the data pipeline. The system can instantiate intelligent routers atthe data pipeline, including services such as solid-state memorydevices, graphical processing units, and/or a packet monitoringfunctions. The system can switch data packets associated with the dataobjects to the intelligent routers, where the data objects are extractedfrom the data packets and stored in cache memory. Upon receivingrequests from client devices and/or other devices and/or networkelements, the intelligent routers can transmit the extracted dataobjects from the cache memory to the client devices. The intelligentrouters can, in turn, be decommissioned subsequent to transmission ofthe extracted data objects.

In one or more embodiments, the system 200 can include a data pipeline202. The data pipeline 202 can include routers for transmitting datapackets. The data pipeline 202 can also include transmission media, suchas fiber optic lines and/or copper wire. In one or more embodiments, thedata pipeline 202 can further include an analytical router 204. Theanalytical router 204 can process packet data streams in the datapipeline in real-time. In particular, analytical router 204 can analyzeheaders from data packets to identify data objects 210 and 210′ that areassociated with the data packets flowing in the data pipeline 202. Forexample, the analytical router 204 can monitor data packets flowing inthe data pipeline 202 to determine if those data packets are part of adata object 210, such as a video stream. In one or more embodiments, theanalytical router 204 can analyze header information from the datapackets. The analytical router 204 can determine if the headerinformation indicates that the data packets are associated with aparticular type of data object 210. For example, header information,such as metadata, title data, data type information, can be gleaned fromthe data packet header, and can be associated with one or more types ofdata objects 210.

In one or more embodiments, the data pipeline 202 may be a User PlaneFunction (UPF) included as a part of a 5G Core infrastructurearchitecture. The UPF may implement a Control and User Plane Separation(CUPS) strategy, where Control Plane (CP) and User Plane (UP) functionsare decoupled, such that functions associated with data forwarding aredecentralized from CP functions. As a result, packet processing andtraffic aggregation may be performed in close proximity to a networkedge. By moving the functions of the data pipeline 202 close to thenetwork edge, the UPF can increase bandwidth efficiencies.

In one or more embodiments, the analytical router 204 can access datamodels 206 that associate various types and/or sequences of header datainformation with various types of data objects, such as video streams.The analytical router 204 can compare the header data informationacquired from data packets it has sampled from the data pipeline withheader data from the data models 206. If the analytical router 204matches or nearly matches the data model header information and the datapacket header information, then the analytical router 204 can concludethat the sampled data packets from the data pipeline 202 are associatedwith a particular type of data object 210.

In one or more embodiments, the data models 206 can be generated via avideo stream training function 208. As header information of packet datais analyzed, target object metadata of the header information andparticular objects 210 can be mutually associated and logged as taggedobjects in a database 212. The video stream training function 208 canuse the mutual association between metadata in the header informationand data objects 210 to train the data models 206 that are used by theanalytical router 204 for identifying new data objects in real-time.

FIG. 2B further depicts an illustrative embodiment of a system 220 forfacilitating, in whole or in part, processing data in a data pipeline.In one or more embodiments, the system 220 can perform real-time taggingof data objects in the data packets of the data pipeline 202. In oneembodiment, a series of analytical routers 204, 204′, and 204″ cananalyze packet data at the data pipeline 202. The analytical router 204can use the trained data models 206 to determine if the data packets atthe data pipeline 204 are associated data objects. The data packets arethereby tagged as belonging to data objects, such as video objects.

In one or more embodiments, upon determining, based on headerinformation, that a group of data packets in the data pipeline 202 iscarrying payload data associated with a data object, the analytic router204 can direct the data packets to an intelligent router 228, 228′, or228″. The intelligent router 228 can include one or more higher leveldevices for processing the data packets at the data object level. Forexample, the intelligent router 228 can include a packet monitoringdevice 232 for extracting data object data from the data packet streams,a solid-state memory device 234 for storing data object data, and/or agraphical processing unit 236 for performing graphical and/ormathematical functions, such as image pattern recognition, on the dataobject data. In one or more embodiments, the generating, obtainingand/or monitoring of this information can be responsive to anauthorization provided by the user. In one or more embodiments, ananalysis of data can be subject to authorization from user(s) associatedwith the data, such as an opt-in, an opt-out, acknowledgementrequirements, notifications, selective authorization based on types ofdata, and so forth.

In one or more embodiments, once the analytical router 204 identifiesthe data packets that include data object data, the intelligent router228 can extract the data object information from the data packets, sothat the system 220 can perform further processing and/or transmissionof the information at the data object level. A significant amount ofdata pipeline resources—bandwidth, capacity, quality of service—can befreed up from processing massive data objects, such as video streams,while intelligent routers 228, 228′, and 228″ perform processing andre-transmission with high efficiency. FIG. 2C further depicts anillustrative embodiment of a system 240 for facilitating, in whole or inpart, processing data in a data pipeline. In particular, the system 240can dynamically allocate resources for processing intensive activitiesof recognizing data objects and tags andenhancing/redirecting/coding/manipulating data objects that have beenrecognized. The system 240 utilizes controllers 242, 244, 246, 248 forproviding a tagging model, policies, learning, and triggering onexternal events. These front end controllers can be of a relativelystatic set of resources for use by the system 240 providing pipeliningservices. By comparison, the backend controllers 250 and 252 forproviding enhancing/redirecting/coding/manipulating of data objects thathave been identified by the system 240 can be relatively dynamicallyadded/subtracted by processes of instantiation, initiation, and/ordecommissioning of virtual network functions as processing needs andquality of service demands change over time.

In one or more embodiments, long-term increases in data traffic andincreases network demand have been found to keep service providers ofInternet and communication data pipelining busy with network centric &packet forwarding pipeline activities. By comparison, over-the-top (OTT)services, such as web-based streaming services, like those provided byGoogle™, Netflix™, Apple™, and Facebook™ have leveraged applicationservices and cloud computing businesses utilizing centralized cloudsolutions by adding intelligence beyond simple routing of data packetsat computing layers. In addition, 5G application demands can forceservice providers to move toward smarter forwarding pipeline that goesbeyond traditional pipelining layers 1-4. In one or more embodiments,the system 220 provides a multi-Layer network delivery system thatcombines a basic forwarding pipeline 202 with data object specificintelligent routers 228 operating at hardware speeds. The data objectcapabilities of the system 220 can provide a service provider withenhanced delivery speeds and data object focus that can allow theservice provider to move “up the stack” into the application level whilestill providing lower level data pipeline services.

In one or more embodiments, a system 220, based on intelligent routers,can provide a multi-level layer environment that can effectively bypassand/or significantly limit the usage of software stacks. Intelligentrouter 228 can include functions, such as but not limited to, integratedGPU (Graphic Processing Unit) 236, DPU (Date Processing Unit) 232,memory/queuing at a SSD (Solid-State Drive) 234. These functions of theintelligent router 228 can be efficiently performed at very highhardware speed. In addition, the intelligent router 228 can include awide range of programmability options consistent with media cruiserforwarding & analytics pipelines in a multi-level layer.

In one or more embodiments, a video stream, such as a soccer game, canbe detected by the analytical router 204. The analytical router 204 canidentify the objects of the game (ball, players, field, etc.,) in thedata packets. The analytical router 204 can capture metadata related tothe game from headers of the data packets. The analytical router 204 cansimply cache this metadata and can stream this metadata instead ofstreaming the entire set of data packets. The data packets can becaptured by the intelligent router 228, which can use its processingresources 232-236 to process the data packets at the data object level.The result is an object-switching pipeline 202. The object-switchingpipeline 202, can be more efficient than a packet-switching pipeline.The ability to switch at higher level primitives, such as data objects,metadata, and/or video clips can result in better utilization ofwireless communication spectrums, network bandwidth, memory, storage,and/or related resources. The additional computing devices and nodes forthe intelligent routers 228 can be integrated into network resources.Object-level switching can enable the lower layers (L1-L4) of an OSInetwork model to be collapsed into intelligent router 228sub-appliances, such as DPI devices 232, SSD memory devices 234, and/orGPU devices 236.

In one or more embodiments, the system 220 can utilized protocols, suchas RDMA (Remote Direct Memory Access) or enhanced-versions of RDMA, towork across wide area networks with capability to extract data objectsat the boundaries of these data objects (e.g., files, Meta data, and/orvideo). By using extensively-integrated appliances, such as DPI 232, SSDmemory 234, and GPU 236, at the intelligent routers 228, all of theneeded object-level processing can be automated and centralized. Thisconcentration of resources can be leveraged to offer personalization ofservices and to gain maximum efficiency for the service providernetworks while eliminating or reducing the need for the operating system(OS) and other, related, traditional memory stacks.

In one or more embodiments, a switching “fabric” of traditional routerscan be overlaid with intelligent routers 228, including for DPI 232, SSDmemory 234, and/or GPU 236. These intelligent routers 228 can bepartitioned into independent resource functions available as a pool tothe network. As a result, networking and service resources managementschedules can be coordinated using high-level event queues.

In one or more embodiments, the analytical router 204 can capture a setof data packets and can use a DPI function, such as the DPI 232 of theintelligent router 228, to determine the general type of content that isbeing carried by the data packers. The analytical router 204 canidentify and categorize the type of the data object (if such a dataobject exists). For example, the analytical router 204 can determine ifthe payloads of the data packets contain information related to apicture, a video, and/or metadata. Once the analytical router 204 hasdetermined the type of data object, that analytical router 204 and/orthe intelligent router 228 can decide how to process the data packets,via the intelligent router 228, in order to efficiently extract the dataobject.

In one or more embodiments, the object-level pipeline 202 providesintegration of a spine and leaf programmable switching fabric withshared intelligent router 228 appliances. The object-level pipeline 202enables the service provider network to take control of containerized(object level) functions, which are compatible with edge and corecloud-centric platforms. The object-level pipeline 202 can takeadvantages of shared intelligent router 228 appliances that are attachedto the switching fabric, via the domain-specific programming language,such as the P4 language, where the domain-specific programming includesa collection of specific processing flows for the intelligent routersbased on a data modeling signatures for the specific data objects. Inone or more embodiments, the switching fabric can utilize the attachedintelligent router 228 appliances, such as DPI 232, SSD memory 234,and/or GPU 236, to characterize metadata and/or data objects. Theswitching fabric can utilizes the domain-specific programming language,such as the P4 language or a “P4-like” programming language, to perform“look up” operations and/or access “look up” tables, which can be usedto instantiate and/or spin off network and service functions in anadaptive fashion. The look up tables can be based on categorizationssignatures, such as data objects or metadata. As a result, theobject-level pipeline 202 can provide enhanced computing, data storage,and/or address space capabilities. These enhanced capabilities canpotentially eliminate and/or reduce the need for data packet switchingwhile enabling object-level and/or metadata-level switching.

For example, a group of data packet streams can represent a video clip,an image of a person, and/or some other intelligent value. Theobject-level pipelines 202 can carry groups of data packets that canrepresent one or more intelligent events. The inclusion of intelligentrouters in the object-level pipelines can significantly improve datalatency and network utilization, while the control of service functionscan be pushed down to the service provider networks. The extension andapplication of the domain-specific programming language to control ofthe analytical routers 204 and the intelligent routers 228 can enablethe identification of data object targets and the use of plug-ins thatcan allow the object-level pipeline to perform faster, more efficiently,and/or more intelligently, while reducing network utilization by use ofcoding techniques. In one or more embodiments, the system 220 does notneed to send an entire video stream of data packets to a client device.Rather, the system 220 can utilized the additional processing power ofthe intelligent routers 228 at the switch fabric level to efficientlyprocess the packet data and to transmit the content at an object-leveland/or metadata-level.

In one or more embodiments, a system 220 of combined object-level datapipelines can use domain-specific programmability to control intelligentrouter 228 appliances, including DPI, SSD memory, and/or GPU. Datapacket flows can enter the system 220, can be categorized into metadataand/or data objects via or modeling signatures for the object types.Network-level services can be set off from the categorized collection ofpacket flows and can form a data object and/or a metadata object. In oneor more embodiments, data packets can be processed into data objects,which are subsequently tagged for object-level switching by deepernetworking switches or cached for load and optimization. In one or moreembodiments, the system is able to spin up containers associated withthe data-object and/or metadata according to the categories, or types,of objects, which will be used for object-level switching. In anotherembedment, machine-learning (ML) algorithms can be used to performknowledge-based, object-level switching based on one or more factors,including external events and pipeline-accessible metadata.

In one or more embodiments, the system 220 can use programmable (oradvanced programmable) extension tables. The extension tables can bedesigned to embrace integrated switching with DPI 232, SSD memory 234,and/or GPUs, where the programmability enables the intelligent router228 to operate at hardware speeds, yet with extremely flexibleprogrammability. For example, a table descriptor can be used to providemulti-layer instructions to each respective hardware component 232 inthe intelligent router 228. The extension tables can function as “DNA”instructions for configuring and controlling the hardware components 232processing any layer of the data object at hardware speeds for both theintelligent router 228 and for other switches and routers. The “DNA”Instructions can describe actions to be performed by the varioushardware in the various scenarios. In various examples, the tableinstructions can direct switches to perform switching function based onIP layers. The table instructions can direct SSD memories 234 canperform storage functions, including advantageous hardware accelerationfeatures, in support of Quality-of-Service (QoS), traffic management,and/or delivery diversity. The memory instructions can be used toperform data queue management and/or “glue logic” to minimize the datacopying and/or the number of copies of data across the various hardwarecomponents of the intelligent router 228. The table instructions candirect DPUs 232 apply packet monitoring to one or more data packets. Thetable instructions can direct GPUs 236 to perform graphical computationand/or parallel graphical computational processing. In one or moreembodiments, the domain-specific language, which can be used forpacket-level switching, can further be extended to direct operations forinspecting payloads, controlling memories, and/or overseeing graphicalfunctions. In one embodiment, data packets can be subject to encryption.In such a case, the system 220 can use machine learning to identifypatterns and/or signatures that can be used by the system 220 toovercome the encryption.

In one example, the table extensions can include instructions, such asswitch control, which can match information in one or more headers ofdata packets. The header information can perform actions for forwardingpipeline data to one or more logical ports, including SSD memory 234,GPU 236, and/or DPU 232 hardware functions. The functions at theintelligent routers can be used to process payloads of data packets (asopposed to dumbly forwarding the data packets). The functions can beused to match logical addresses and/or to copy data from a logical portinto an addressable memory. The memory pointers can be used asreferences by other components. The functions at the intelligent routers228 can be used at the SSD 234 to compare and/or match specific SSDfeatures or actions, such as storing, organizing, scheduling deliveries,and/or categorizing data and/or objects.

The functions at the intelligent routers 228 can be used to direct a GPU236 to perform graphical functions. The functions at the intelligentrouters 228 can be used to direct the DPU to perform packet monitoringof one or more data packets in order to compare and/or match data,including categorization of metadata and/or tagging. Intelligent routerequipment and functions are described in Table 1, below:

TABLE 1 Intelligent Router equipment and functions. Switch Forwardingpipeline Match/action (forwarding) SSD store and compute pipelineMatch/Action (compute and delivery) GPU compute instruction pipelineMach/Action (Compute and . . . ) DPI; instruction pipeline Mach/Action(shift into payload and action)

In one or more embodiments, a packet data source at the pipeline 202 cantrigger different types of application containers based on differenttypes of data objects, metadata, and/or flow signatures detected by theanalytical router 204 at the pipeline 202. In one example, thedomain-specific programming language instructions can direct the system202 to categorize the data packets and/or automatically processpre-labeled packets into objects. In this example, the data objects canbe handled according to level policies and/according to policydefinitions. This architecture can enable shared platforms at a networkedge to meet standards for application performance, latency, scaling,optimization, user data privacy and/or security.

In one or more embodiments, edge functionality can be implemented toexploit new technology trends and/or enhance opportunities beyond simple“dumb” pipes. In one example, applications executing at user equipment(UE) and/or internet-of-things (IOT) devices can utilizes edge functionsby using network protocols, such RDMA or simple rest APIs. Serviceproviders can complement intelligent routers 228 by adding applications,such as intelligent street light gateways to create a data-driventransportation solution.

In one or more embodiments, the system 220 can facilitate mesh-formingdata collectors that can enable dynamic configuration of instructionsthat can match instruction “DNA” sets. These “DNA” instruction sets canbe received by the system 220 every period from a Software DefinedCollector controller. In one or more embodiments, a ML-centric approachcan be used to establish programmable “super tables,” which can be usedfor multi-layer networking. Ambient, or background, computing at theintelligent routers 228 can facilitate applications, which incorporateML and/or other forms of artificial intelligence (AI). The ML can becharacterized by features for human-like cognitive, behavioralcapabilities, and/or contextual awareness. A digital environment can becreated, in which companies can integrate technology seamlessly and/orinvisibly into a large variety of devices to maximize usefulness whileminimizing demands for attention or maintenance.

In one or more embodiments, the system 220 can support a number of usecases. For example, the intelligent router 228 can include an inline DPUpipeline with a P4-based instruction wrapper. The intelligent router 228can also include Inline object switching pipeline with a P4 wrapper,inline ML inference pipeline with an AI wrapper, and an inline Codecpipeline with IP security (IPSEC)/Secure Sockets Layer (SSL) and a P4wrapper. The intelligent router 228 van also include P4 Interrupt/VProbeobservability with a P4 wrapper and/or P4 Video/Voice/IOT Compressionwith a P4 Wrapper. In this example, an extension “super” table can beused to establish flow categorization of flows. The intelligent router228 can perform operations for dynamically spinning-off or instantiatingmultilayer hardware level functions. The hardware level functions can bedynamically provided on per service and/or per flow basis. Theintelligent router can perform operations for re-programming theextended tables, for association extended tables to specific probesand/or compute functions, for accessing a “DNA” data model, key matricesand/or data probes, for controlling delivery mechanisms, for performingoperations for computing, ML, aggregation of object data, and/orgenerating entities.

FIG. 2D depicts an illustrative embodiment of a method 260 in accordancewith various aspects described herein. A system 220 can monitor datapackets in a data pipeline, in step 262. In step 264, the system 220 canidentify data objects in the data packets. For example, the system 220can analyze header information from data packets to detect indicatorsfor a type of data object, such a metadata. In step 266, the analyticrouter 204 can instantiate an intelligent router 228 at the datapipeline and switch the set of data packets identified to the dataobject to the intelligent router 228 for processing. If the intelligentrouter 228 is already extant at the data pipeline, then the system 220can simply direct the set of data packets to the intelligent router 228.In step 268, the intelligent router 228 can extract the data object fromthe set of data packets.

In step 270, the system 220 can receive a request from a client devicefor a data object. The system 220 can respond to the request bytransmitting the extracted data object to the client device, in step272. In step 274, the system 220 can decommission the intelligent router228 at the conclusion of the extraction and/or transmission of theextracted data object.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIG. 2D, itis to be understood and appreciated that the claimed subject matter isnot limited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methods described herein.

Referring now to FIG. 3, a block diagram 300 is shown illustrating anexample, non-limiting embodiment of a virtualized communication networkin accordance with various aspects described herein. In particular avirtualized communication network is presented that can be used toimplement some or all of the subsystems and functions of communicationnetwork 100, the subsystems and functions of system 200, and method 260presented in FIGS. 1, 2A-2D, and 3. For example, communications network100 can facilitate in whole or in part processing data in a datapipeline. A system can monitor a data pipeline to identify data objectsin data packets carried by the data pipeline. The system can instantiateintelligent routers at the data pipeline, including services such assolid-state memory devices, graphical processing units, and/or a packetmonitoring functions. The system can switch data packets associated withthe data objects to the intelligent routers, where the data objects areextracted from the data packets and stored in cache memory. Uponreceiving requests from client devices, the intelligent routers cantransmit the extracted data objects from the cache memory to the clientdevices. The intelligent routers can, in turn, be decommissionedsubsequent to transmission of the extracted data objects.

In particular, a cloud networking architecture is shown that leveragescloud technologies and supports rapid innovation and scalability via atransport layer 350, a virtualized network function cloud 325 and/or oneor more cloud computing environments 375. In various embodiments, thiscloud networking architecture is an open architecture that leveragesapplication programming interfaces (APIs); reduces complexity fromservices and operations; supports more nimble business models; andrapidly and seamlessly scales to meet evolving customer requirementsincluding traffic growth, diversity of traffic types, and diversity ofperformance and reliability expectations.

In contrast to traditional network elements—which are typicallyintegrated to perform a single function, the virtualized communicationnetwork employs virtual network elements (VNEs) 330, 332, 334, etc. thatperform some or all of the functions of network elements 150, 152, 154,156, etc. For example, the network architecture can provide a substrateof networking capability, often called Network Function VirtualizationInfrastructure (NFVI) or simply infrastructure that is capable of beingdirected with software and Software Defined Networking (SDN) protocolsto perform a broad variety of network functions and services. Thisinfrastructure can include several types of substrates. The most typicaltype of substrate being servers that support Network FunctionVirtualization (NFV), followed by packet forwarding capabilities basedon generic computing resources, with specialized network technologiesbrought to bear when general purpose processors or general purposeintegrated circuit devices offered by merchants (referred to herein asmerchant silicon) are not appropriate. In this case, communicationservices can be implemented as cloud-centric workloads.

As an example, a traditional network element 150 (shown in FIG. 1), suchas an edge router can be implemented via a VNE 330 composed of NFVsoftware modules, merchant silicon, and associated controllers. Thesoftware can be written so that increasing workload consumes incrementalresources from a common resource pool, and moreover so that it'selastic: so the resources are only consumed when needed. In a similarfashion, other network elements such as other routers, switches, edgecaches, and middle-boxes are instantiated from the common resource pool.Such sharing of infrastructure across a broad set of uses makes planningand growing infrastructure easier to manage.

In an embodiment, the transport layer 350 includes fiber, cable, wiredand/or wireless transport elements, network elements and interfaces toprovide broadband access 110, wireless access 120, voice access 130,media access 140 and/or access to content sources 175 for distributionof content to any or all of the access technologies. In particular, insome cases a network element needs to be positioned at a specific place,and this allows for less sharing of common infrastructure. Other times,the network elements have specific physical layer adapters that cannotbe abstracted or virtualized, and might require special DSP code andanalog front-ends (AFEs) that do not lend themselves to implementationas VNEs 330, 332 or 334. These network elements can be included intransport layer 350.

The virtualized network function cloud 325 interfaces with the transportlayer 350 to provide the VNEs 330, 332, 334, etc. to provide specificNFVs. In particular, the virtualized network function cloud 325leverages cloud operations, applications, and architectures to supportnetworking workloads. The virtualized network elements 330, 332 and 334can employ network function software that provides either a one-for-onemapping of traditional network element function or alternately somecombination of network functions designed for cloud computing. Forexample, VNEs 330, 332 and 334 can include route reflectors, domain namesystem (DNS) servers, and dynamic host configuration protocol (DHCP)servers, system architecture evolution (SAE) and/or mobility managemententity (MME) gateways, broadband network gateways, IP edge routers forIP-VPN, Ethernet and other services, load balancers, distributers andother network elements. Because these elements don't typically need toforward large amounts of traffic, their workload can be distributedacross a number of servers—each of which adds a portion of thecapability, and overall which creates an elastic function with higheravailability than its former monolithic version. These virtual networkelements 330, 332, 334, etc. can be instantiated and managed using anorchestration approach similar to those used in cloud compute services.

The cloud computing environments 375 can interface with the virtualizednetwork function cloud 325 via APIs that expose functional capabilitiesof the VNEs 330, 332, 334, etc. to provide the flexible and expandedcapabilities to the virtualized network function cloud 325. Inparticular, network workloads may have applications distributed acrossthe virtualized network function cloud 325 and cloud computingenvironment 375 and in the commercial cloud, or might simply orchestrateworkloads supported entirely in NFV infrastructure from these thirdparty locations.

Turning now to FIG. 4, there is illustrated a block diagram of acomputing environment in accordance with various aspects describedherein. In order to provide additional context for various embodimentsof the embodiments described herein, FIG. 4 and the following discussionare intended to provide a brief, general description of a suitablecomputing environment 400 in which the various embodiments of thesubject disclosure can be implemented. In particular, computingenvironment 400 can be used in the implementation of network elements150, 152, 154, 156, access terminal 112, base station or access point122, switching device 132, media terminal 142, and/or VNEs 330, 332,334, etc. Each of these devices can be implemented viacomputer-executable instructions that can run on one or more computers,and/or in combination with other program modules and/or as a combinationof hardware and software. For example, communications network 100 canfacilitate in whole or in part processing data in a data pipeline. Asystem can monitor a data pipeline to identify data objects in datapackets carried by the data pipeline. The system can instantiateintelligent routers at the data pipeline, including services such assolid-state memory devices, graphical processing units, and/or a packetmonitoring functions. The system can switch data packets associated withthe data objects to the intelligent routers, where the data objects areextracted from the data packets and stored in cache memory. Uponreceiving requests from client devices, the intelligent routers cantransmit the extracted data objects from the cache memory to the clientdevices. The intelligent routers can, in turn, be decommissionedsubsequent to transmission of the extracted data objects.

Generally, program modules comprise routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the methods can be practiced with other computer systemconfigurations, comprising single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices.

As used herein, a processing circuit includes one or more processors aswell as other application specific circuits such as an applicationspecific integrated circuit, digital logic circuit, state machine,programmable gate array or other circuit that processes input signals ordata and that produces output signals or data in response thereto. Itshould be noted that while any functions and features described hereinin association with the operation of a processor could likewise beperformed by a processing circuit.

The illustrated embodiments of the embodiments herein can be alsopracticed in distributed computing environments where certain tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules can be located in both local and remote memory storage devices.

Computing devices typically comprise a variety of media, which cancomprise computer-readable storage media and/or communications media,which two terms are used herein differently from one another as follows.Computer-readable storage media can be any available storage media thatcan be accessed by the computer and comprises both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structured dataor unstructured data.

Computer-readable storage media can comprise, but are not limited to,random access memory (RAM), read only memory (ROM), electricallyerasable programmable read only memory (EEPROM), flash memory or othermemory technology, compact disk read only memory (CD-ROM), digitalversatile disk (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devicesor other tangible and/or non-transitory media which can be used to storedesired information. In this regard, the terms “tangible” or“non-transitory” herein as applied to storage, memory orcomputer-readable media, are to be understood to exclude onlypropagating transitory signals per se as modifiers and do not relinquishrights to all standard storage, memory or computer-readable media thatare not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and comprises any informationdelivery or transport media. The term “modulated data signal” or signalsrefers to a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in one or moresignals. By way of example, and not limitation, communication mediacomprise wired media, such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared and otherwireless media.

With reference again to FIG. 4, the example environment can comprise acomputer 402, the computer 402 comprising a processing unit 404, asystem memory 406 and a system bus 408. The system bus 408 couplessystem components including, but not limited to, the system memory 406to the processing unit 404. The processing unit 404 can be any ofvarious commercially available processors. Dual microprocessors andother multiprocessor architectures can also be employed as theprocessing unit 404.

The system bus 408 can be any of several types of bus structure that canfurther interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 406comprises ROM 410 and RAM 412. A basic input/output system (BIOS) can bestored in a non-volatile memory such as ROM, erasable programmable readonly memory (EPROM), EEPROM, which BIOS contains the basic routines thathelp to transfer information between elements within the computer 402,such as during startup. The RAM 412 can also comprise a high-speed RAMsuch as static RAM for caching data.

The computer 402 further comprises an internal hard disk drive (HDD) 414(e.g., EIDE, SATA), which internal HDD 414 can also be configured forexternal use in a suitable chassis (not shown), a magnetic floppy diskdrive (FDD) 416, (e.g., to read from or write to a removable diskette418) and an optical disk drive 420, (e.g., reading a CD-ROM disk 422 or,to read from or write to other high capacity optical media such as theDVD). The HDD 414, magnetic FDD 416 and optical disk drive 420 can beconnected to the system bus 408 by a hard disk drive interface 424, amagnetic disk drive interface 426 and an optical drive interface 428,respectively. The hard disk drive interface 424 for external driveimplementations comprises at least one or both of Universal Serial Bus(USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394interface technologies. Other external drive connection technologies arewithin contemplation of the embodiments described herein.

The drives and their associated computer-readable storage media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 402, the drives and storagemedia accommodate the storage of any data in a suitable digital format.Although the description of computer-readable storage media above refersto a hard disk drive (HDD), a removable magnetic diskette, and aremovable optical media such as a CD or DVD, it should be appreciated bythose skilled in the art that other types of storage media which arereadable by a computer, such as zip drives, magnetic cassettes, flashmemory cards, cartridges, and the like, can also be used in the exampleoperating environment, and further, that any such storage media cancontain computer-executable instructions for performing the methodsdescribed herein.

A number of program modules can be stored in the drives and RAM 412,comprising an operating system 430, one or more application programs432, other program modules 434 and program data 436. All or portions ofthe operating system, applications, modules, and/or data can also becached in the RAM 412. The systems and methods described herein can beimplemented utilizing various commercially available operating systemsor combinations of operating systems.

A user can enter commands and information into the computer 402 throughone or more wired/wireless input devices, e.g., a keyboard 438 and apointing device, such as a mouse 440. Other input devices (not shown)can comprise a microphone, an infrared (IR) remote control, a joystick,a game pad, a stylus pen, touch screen or the like. These and otherinput devices are often connected to the processing unit 404 through aninput device interface 442 that can be coupled to the system bus 408,but can be connected by other interfaces, such as a parallel port, anIEEE 1394 serial port, a game port, a universal serial bus (USB) port,an IR interface, etc.

A monitor 444 or other type of display device can be also connected tothe system bus 408 via an interface, such as a video adapter 446. Itwill also be appreciated that in alternative embodiments, a monitor 444can also be any display device (e.g., another computer having a display,a smart phone, a tablet computer, etc.) for receiving displayinformation associated with computer 402 via any communication means,including via the Internet and cloud-based networks. In addition to themonitor 444, a computer typically comprises other peripheral outputdevices (not shown), such as speakers, printers, etc.

The computer 402 can operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 448. The remotecomputer(s) 448 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallycomprises many or all of the elements described relative to the computer402, although, for purposes of brevity, only a remote memory/storagedevice 450 is illustrated. The logical connections depicted comprisewired/wireless connectivity to a local area network (LAN) 452 and/orlarger networks, e.g., a wide area network (WAN) 454. Such LAN and WANnetworking environments are commonplace in offices and companies, andfacilitate enterprise-wide computer networks, such as intranets, all ofwhich can connect to a global communications network, e.g., theInternet.

When used in a LAN networking environment, the computer 402 can beconnected to the LAN 452 through a wired and/or wireless communicationnetwork interface or adapter 456. The adapter 456 can facilitate wiredor wireless communication to the LAN 452, which can also comprise awireless AP disposed thereon for communicating with the adapter 456.

When used in a WAN networking environment, the computer 402 can comprisea modem 458 or can be connected to a communications server on the WAN454 or has other means for establishing communications over the WAN 454,such as by way of the Internet. The modem 458, which can be internal orexternal and a wired or wireless device, can be connected to the systembus 408 via the input device interface 442. In a networked environment,program modules depicted relative to the computer 402 or portionsthereof, can be stored in the remote memory/storage device 450. It willbe appreciated that the network connections shown are example and othermeans of establishing a communications link between the computers can beused.

The computer 402 can be operable to communicate with any wirelessdevices or entities operatively disposed in wireless communication,e.g., a printer, scanner, desktop and/or portable computer, portabledata assistant, communications satellite, any piece of equipment orlocation associated with a wirelessly detectable tag (e.g., a kiosk,news stand, restroom), and telephone. This can comprise WirelessFidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, thecommunication can be a predefined structure as with a conventionalnetwork or simply an ad hoc communication between at least two devices.

Wi-Fi can allow connection to the Internet from a couch at home, a bedin a hotel room or a conference room at work, without wires. Wi-Fi is awireless technology similar to that used in a cell phone that enablessuch devices, e.g., computers, to send and receive data indoors and out;anywhere within the range of a base station. Wi-Fi networks use radiotechnologies called IEEE 802.11 (a, b, g, n, ac, ag, etc.,) to providesecure, reliable, fast wireless connectivity. A Wi-Fi network can beused to connect computers to each other, to the Internet, and to wirednetworks (which can use IEEE 802.3 or Ethernet). Wi-Fi networks operatein the unlicensed 2.4 and 5 GHz radio bands for example or with productsthat contain both bands (dual band), so the networks can providereal-world performance similar to the basic 10BaseT wired Ethernetnetworks used in many offices.

Turning now to FIG. 5, an embodiment 500 of a mobile network platform510 is shown that is an example of network elements 150, 152, 154, 156,and/or VNEs 330, 332, 334, etc. For example, platform 510 canfacilitate, in whole or in part, processing data in a data pipeline. Asystem can monitor a data pipeline to identify data objects in datapackets carried by the data pipeline. The system can instantiateintelligent routers at the data pipeline, including services such assolid-state memory devices, graphical processing units, and/or a packetmonitoring functions. The system can switch data packets associated withthe data objects to the intelligent routers, where the data objects areextracted from the data packets and stored in cache memory. Uponreceiving requests from client devices, the intelligent routers cantransmit the extracted data objects from the cache memory to the clientdevices. The intelligent routers can, in turn, be decommissionedsubsequent to transmission of the extracted data objects.

In one or more embodiments, the mobile network platform 510 can generateand receive signals transmitted and received by base stations or accesspoints such as base station or access point 122. Generally, mobilenetwork platform 510 can comprise components, e.g., nodes, gateways,interfaces, servers, or disparate platforms, that facilitate bothpacket-switched (PS) (e.g., internet protocol (IP), frame relay,asynchronous transfer mode (ATM)) and circuit-switched (CS) traffic(e.g., voice and data), as well as control generation for networkedwireless telecommunication. As a non-limiting example, mobile networkplatform 510 can be included in telecommunications carrier networks, andcan be considered carrier-side components as discussed elsewhere herein.Mobile network platform 510 comprises CS gateway node(s) 512 which caninterface CS traffic received from legacy networks like telephonynetwork(s) 540 (e.g., public switched telephone network (PSTN), orpublic land mobile network (PLMN)) or a signaling system #7 (SS7)network 560. CS gateway node(s) 512 can authorize and authenticatetraffic (e.g., voice) arising from such networks. Additionally, CSgateway node(s) 512 can access mobility, or roaming, data generatedthrough SS7 network 560; for instance, mobility data stored in a visitedlocation register (VLR), which can reside in memory 530. Moreover, CSgateway node(s) 512 interfaces CS-based traffic and signaling and PSgateway node(s) 518. As an example, in a 3GPP UMTS network, CS gatewaynode(s) 512 can be realized at least in part in gateway GPRS supportnode(s) (GGSN). It should be appreciated that functionality and specificoperation of CS gateway node(s) 512, PS gateway node(s) 518, and servingnode(s) 516, is provided and dictated by radio technology(ies) utilizedby mobile network platform 510 for telecommunication over a radio accessnetwork 520 with other devices, such as a radiotelephone 575.

The embodiment 500 may support and/or operate according to a 5G system(5GS). A 5GS can include Next Generation (NG) functions, such as aNG-RAN and a NG-CORE. For example, the NG-RAN may consists of gNBs, alsoknown as New Radio (NR) base stations, and/or NG-eNBs. The NG-eNBs mayinclude LTE base stations capable of supporting a 5G Core Network. TheNG-RAN may be capable of supporting network slicing and/or aspectsrelated to QoS flow management and/or mapping to radio bearers. TheNG-CORE may provide capabilities for full separation between the ControlPlane (CP) and the User Plane (UP). For example, a Session ManagementFunction (SMF) may include session management functions and/or UE IPAddress function, which may be provided in an LTE system via MME and/orPGW functions. An Access and Mobility Management Function (AMF) mayinclude mobility management and network access functions, registrationfunctions, and/or security functions. A User Plane Function (UPF) mayinclude network functions performing purely packet processing andtransmission operations for the data plane. The NG-RAN and NG-CORE caninterface via an NG interface. The NG interface may be either of twoversions, NG-C or NG-U, which may be connected to to the AMF and theUPF.

In addition to receiving and processing CS-switched traffic andsignaling, PS gateway node(s) 518 can authorize and authenticatePS-based data sessions with served mobile devices. Data sessions cancomprise traffic, or content(s), exchanged with networks external to themobile network platform 510, like wide area network(s) (WANs) 550,enterprise network(s) 570, and service network(s) 580, which can beembodied in local area network(s) (LANs), can also be interfaced withmobile network platform 510 through PS gateway node(s) 518. It is to benoted that WANs 550 and enterprise network(s) 570 can embody, at leastin part, a service network(s) like IP multimedia subsystem (IMS). Basedon radio technology layer(s) available in technology resource(s) orradio access network 520, PS gateway node(s) 518 can generate packetdata protocol contexts when a data session is established; other datastructures that facilitate routing of packetized data also can begenerated. To that end, in an aspect, PS gateway node(s) 518 cancomprise a tunnel interface (e.g., tunnel termination gateway (TTG) in3GPP UMTS network(s) (not shown)) which can facilitate packetizedcommunication with disparate wireless network(s), such as Wi-Finetworks.

In embodiment 500, mobile network platform 510 also comprises servingnode(s) 516 that, based upon available radio technology layer(s) withintechnology resource(s) in the radio access network 520, convey thevarious packetized flows of data streams received through PS gatewaynode(s) 518. It is to be noted that for technology resource(s) that relyprimarily on CS communication, server node(s) can deliver trafficwithout reliance on PS gateway node(s) 518; for example, server node(s)can embody at least in part a mobile switching center. As an example, ina 3GPP UMTS network, serving node(s) 516 can be embodied in serving GPRSsupport node(s) (SGSN).

For radio technologies that exploit packetized communication, server(s)514 in mobile network platform 510 can execute numerous applicationsthat can generate multiple disparate packetized data streams or flows,and manage (e.g., schedule, queue, format . . . ) such flows. Suchapplication(s) can comprise add-on features to standard services (forexample, provisioning, billing, customer support . . . ) provided bymobile network platform 510. Data streams (e.g., content(s) that arepart of a voice call or data session) can be conveyed to PS gatewaynode(s) 518 for authorization/authentication and initiation of a datasession, and to serving node(s) 516 for communication thereafter. Inaddition to application server, server(s) 514 can comprise utilityserver(s), a utility server can comprise a provisioning server, anoperations and maintenance server, a security server that can implementat least in part a certificate authority and firewalls as well as othersecurity mechanisms, and the like. In an aspect, security server(s)secure communication served through mobile network platform 510 toensure network's operation and data integrity in addition toauthorization and authentication procedures that CS gateway node(s) 512and PS gateway node(s) 518 can enact. Moreover, provisioning server(s)can provision services from external network(s) like networks operatedby a disparate service provider; for instance, WAN 550 or GlobalPositioning System (GPS) network(s) (not shown). Provisioning server(s)can also provision coverage through networks associated to mobilenetwork platform 510 (e.g., deployed and operated by the same serviceprovider), such as the distributed antennas networks shown in FIG. 1(s)that enhance wireless service coverage by providing more networkcoverage.

It is to be noted that server(s) 514 can comprise one or more processorsconfigured to confer at least in part the functionality of mobilenetwork platform 510. To that end, the one or more processor can executecode instructions stored in memory 530, for example. It is should beappreciated that server(s) 514 can comprise a content manager, whichoperates in substantially the same manner as described hereinbefore.

In example embodiment 500, memory 530 can store information related tooperation of mobile network platform 510. Other operational informationcan comprise provisioning information of mobile devices served throughmobile network platform 510, subscriber databases; applicationintelligence, pricing schemes, e.g., promotional rates, flat-rateprograms, couponing campaigns; technical specification(s) consistentwith telecommunication protocols for operation of disparate radio, orwireless, technology layers; and so forth. Memory 530 can also storeinformation from at least one of telephony network(s) 540, WAN 550, SS7network 560, or enterprise network(s) 570. In an aspect, memory 530 canbe, for example, accessed as part of a data store component or as aremotely connected memory store.

In order to provide a context for the various aspects of the disclosedsubject matter, FIG. 5, and the following discussion, are intended toprovide a brief, general description of a suitable environment in whichthe various aspects of the disclosed subject matter can be implemented.While the subject matter has been described above in the general contextof computer-executable instructions of a computer program that runs on acomputer and/or computers, those skilled in the art will recognize thatthe disclosed subject matter also can be implemented in combination withother program modules. Generally, program modules comprise routines,programs, components, data structures, etc. that perform particulartasks and/or implement particular abstract data types.

Turning now to FIG. 6, an illustrative embodiment of a communicationdevice 600 is shown. The communication device 600 can serve as anillustrative embodiment of devices such as data terminals 114, mobiledevices 124, vehicle 126, display devices 144 or other client devicesfor communication via either communications network 125. For example,computing device 600 can facilitate, in whole or in part, equipment forprocessing data in a data pipeline. A system can monitor a data pipelineto identify data objects in data packets carried by the data pipeline.The system can instantiate intelligent routers at the data pipeline,including services such as solid-state memory devices, graphicalprocessing units, and/or a packet monitoring functions. The system canswitch data packets associated with the data objects to the intelligentrouters, where the data objects are extracted from the data packets andstored in cache memory. Upon receiving requests from client devices, theintelligent routers can transmit the extracted data objects from thecache memory to the client devices. The intelligent routers can, inturn, be decommissioned subsequent to transmission of the extracted dataobjects.

The communication device 600 can comprise a wireline and/or wirelesstransceiver 602 (herein transceiver 602), a user interface (UI) 604, apower supply 614, a location receiver 616, a motion sensor 618, anorientation sensor 620, and a controller 606 for managing operationsthereof. The transceiver 602 can support short-range or long-rangewireless access technologies such as Bluetooth®, ZigBee®, WiFi, DECT, orcellular communication technologies, just to mention a few (Bluetooth®and ZigBee® are trademarks registered by the Bluetooth® Special InterestGroup and the ZigBee® Alliance, respectively). Cellular technologies caninclude, for example, CDMA-1X, UMTS/HSDPA, GSM/GPRS, TDMA/EDGE, EV/DO,WiMAX, SDR, LTE, as well as other next generation wireless communicationtechnologies as they arise. The transceiver 602 can also be adapted tosupport circuit-switched wireline access technologies (such as PSTN),packet-switched wireline access technologies (such as TCP/IP, VoIP,etc.), and combinations thereof.

The UI 604 can include a depressible or touch-sensitive keypad 608 witha navigation mechanism such as a roller ball, a joystick, a mouse, or anavigation disk for manipulating operations of the communication device600. The keypad 608 can be an integral part of a housing assembly of thecommunication device 600 or an independent device operably coupledthereto by a tethered wireline interface (such as a USB cable) or awireless interface supporting for example Bluetooth®. The keypad 608 canrepresent a numeric keypad commonly used by phones, and/or a QWERTYkeypad with alphanumeric keys. The UI 604 can further include a display610 such as monochrome or color LCD (Liquid Crystal Display), OLED(Organic Light Emitting Diode) or other suitable display technology forconveying images to an end user of the communication device 600. In anembodiment where the display 610 is touch-sensitive, a portion or all ofthe keypad 608 can be presented by way of the display 610 withnavigation features.

The display 610 can use touch screen technology to also serve as a userinterface for detecting user input. As a touch screen display, thecommunication device 600 can be adapted to present a user interfacehaving graphical user interface (GUI) elements that can be selected by auser with a touch of a finger. The display 610 can be equipped withcapacitive, resistive or other forms of sensing technology to detect howmuch surface area of a user's finger has been placed on a portion of thetouch screen display. This sensing information can be used to controlthe manipulation of the GUI elements or other functions of the userinterface. The display 610 can be an integral part of the housingassembly of the communication device 600 or an independent devicecommunicatively coupled thereto by a tethered wireline interface (suchas a cable) or a wireless interface.

The UI 604 can also include an audio system 612 that utilizes audiotechnology for conveying low volume audio (such as audio heard inproximity of a human ear) and high volume audio (such as speakerphonefor hands free operation). The audio system 612 can further include amicrophone for receiving audible signals of an end user. The audiosystem 612 can also be used for voice recognition applications. The UI604 can further include an image sensor 613 such as a charged coupleddevice (CCD) camera for capturing still or moving images.

The power supply 614 can utilize common power management technologiessuch as replaceable and rechargeable batteries, supply regulationtechnologies, and/or charging system technologies for supplying energyto the components of the communication device 600 to facilitatelong-range or short-range portable communications. Alternatively, or incombination, the charging system can utilize external power sources suchas DC power supplied over a physical interface such as a USB port orother suitable tethering technologies.

The location receiver 616 can utilize location technology such as aglobal positioning system (GPS) receiver capable of assisted GPS foridentifying a location of the communication device 600 based on signalsgenerated by a constellation of GPS satellites, which can be used forfacilitating location services such as navigation. The motion sensor 618can utilize motion sensing technology such as an accelerometer, agyroscope, or other suitable motion sensing technology to detect motionof the communication device 600 in three-dimensional space. Theorientation sensor 620 can utilize orientation sensing technology suchas a magnetometer to detect the orientation of the communication device600 (north, south, west, and east, as well as combined orientations indegrees, minutes, or other suitable orientation metrics).

The communication device 600 can use the transceiver 602 to alsodetermine a proximity to a cellular, WiFi, Bluetooth®, or other wirelessaccess points by sensing techniques such as utilizing a received signalstrength indicator (RSSI) and/or signal time of arrival (TOA) or time offlight (TOF) measurements. The controller 606 can utilize computingtechnologies such as a microprocessor, a digital signal processor (DSP),programmable gate arrays, application specific integrated circuits,and/or a video processor with associated storage memory such as Flash,ROM, RAM, SRAM, DRAM or other storage technologies for executingcomputer instructions, controlling, and processing data supplied by theaforementioned components of the communication device 600.

Other components not shown in FIG. 6 can be used in one or moreembodiments of the subject disclosure. For instance, the communicationdevice 600 can include a slot for adding or removing an identity modulesuch as a Subscriber Identity Module (SIM) card or Universal IntegratedCircuit Card (UICC). SIM or UICC cards can be used for identifyingsubscriber services, executing programs, storing subscriber data, and soon.

The terms “first,” “second,” “third,” and so forth, as used in theclaims, unless otherwise clear by context, is for clarity only anddoesn't otherwise indicate or imply any order in time. For instance, “afirst determination,” “a second determination,” and “a thirddetermination,” does not indicate or imply that the first determinationis to be made before the second determination, or vice versa, etc.

In the subject specification, terms such as “store,” “storage,” “datastore,” data storage,” “database,” and substantially any otherinformation storage component relevant to operation and functionality ofa component, refer to “memory components,” or entities embodied in a“memory” or components comprising the memory. It will be appreciatedthat the memory components described herein can be either volatilememory or nonvolatile memory, or can comprise both volatile andnonvolatile memory, by way of illustration, and not limitation, volatilememory, non-volatile memory, disk storage, and memory storage. Further,nonvolatile memory can be included in read only memory (ROM),programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable ROM (EEPROM), or flash memory. Volatile memory cancomprise random access memory (RAM), which acts as external cachememory. By way of illustration and not limitation, RAM is available inmany forms such as synchronous RAM (SRAM), dynamic RAM (DRAM),synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhancedSDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM).Additionally, the disclosed memory components of systems or methodsherein are intended to comprise, without being limited to comprising,these and any other suitable types of memory.

Moreover, it will be noted that the disclosed subject matter can bepracticed with other computer system configurations, comprisingsingle-processor or multiprocessor computer systems, mini-computingdevices, mainframe computers, as well as personal computers, hand-heldcomputing devices (e.g., PDA, phone, smartphone, watch, tabletcomputers, netbook computers, etc.), microprocessor-based orprogrammable consumer or industrial electronics, and the like. Theillustrated aspects can also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network; however, some if not allaspects of the subject disclosure can be practiced on stand-alonecomputers. In a distributed computing environment, program modules canbe located in both local and remote memory storage devices.

In one or more embodiments, information regarding use of services can begenerated including services being accessed, media consumption history,user preferences, and so forth. This information can be obtained byvarious methods including user input, detecting types of communications(e.g., video content vs. audio content), analysis of content streams,sampling, and so forth. The generating, obtaining and/or monitoring ofthis information can be responsive to an authorization provided by theuser. In one or more embodiments, an analysis of data can be subject toauthorization from user(s) associated with the data, such as an opt-in,an opt-out, acknowledgement requirements, notifications, selectiveauthorization based on types of data, and so forth.

Some of the embodiments described herein can also employ artificialintelligence (AI) to facilitate automating one or more featuresdescribed herein. The embodiments (e.g., in connection withautomatically identifying acquired cell sites that provide a maximumvalue/benefit after addition to an existing communication network) canemploy various AI-based schemes for carrying out various embodimentsthereof. Moreover, the classifier can be employed to determine a rankingor priority of each cell site of the acquired network. A classifier is afunction that maps an input attribute vector, x=(x1, x2, x3, x4, . . . ,xn), to a confidence that the input belongs to a class, that is,f(x)=confidence (class). Such classification can employ a probabilisticand/or statistical-based analysis (e.g., factoring into the analysisutilities and costs) to determine or infer an action that a user desiresto be automatically performed. A support vector machine (SVM) is anexample of a classifier that can be employed. The SVM operates byfinding a hypersurface in the space of possible inputs, which thehypersurface attempts to split the triggering criteria from thenon-triggering events. Intuitively, this makes the classificationcorrect for testing data that is near, but not identical to trainingdata. Other directed and undirected model classification approachescomprise, e.g., naïve Bayes, Bayesian networks, decision trees, neuralnetworks, fuzzy logic models, and probabilistic classification modelsproviding different patterns of independence can be employed.Classification as used herein also is inclusive of statisticalregression that is utilized to develop models of priority.

As will be readily appreciated, one or more of the embodiments canemploy classifiers that are explicitly trained (e.g., via a generictraining data) as well as implicitly trained (e.g., via observing UEbehavior, operator preferences, historical information, receivingextrinsic information). For example, SVMs can be configured via alearning or training phase within a classifier constructor and featureselection module. Thus, the classifier(s) can be used to automaticallylearn and perform a number of functions, including but not limited todetermining according to predetermined criteria which of the acquiredcell sites will benefit a maximum number of subscribers and/or which ofthe acquired cell sites will add minimum value to the existingcommunication network coverage, etc.

As used in some contexts in this application, in some embodiments, theterms “component,” “system” and the like are intended to refer to, orcomprise, a computer-related entity or an entity related to anoperational apparatus with one or more specific functionalities, whereinthe entity can be either hardware, a combination of hardware andsoftware, software, or software in execution. As an example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution,computer-executable instructions, a program, and/or a computer. By wayof illustration and not limitation, both an application running on aserver and the server can be a component. One or more components mayreside within a process and/or thread of execution and a component maybe localized on one computer and/or distributed between two or morecomputers. In addition, these components can execute from variouscomputer readable media having various data structures stored thereon.The components may communicate via local and/or remote processes such asin accordance with a signal having one or more data packets (e.g., datafrom one component interacting with another component in a local system,distributed system, and/or across a network such as the Internet withother systems via the signal). As another example, a component can be anapparatus with specific functionality provided by mechanical partsoperated by electric or electronic circuitry, which is operated by asoftware or firmware application executed by a processor, wherein theprocessor can be internal or external to the apparatus and executes atleast a part of the software or firmware application. As yet anotherexample, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,the electronic components can comprise a processor therein to executesoftware or firmware that confers at least in part the functionality ofthe electronic components. While various components have beenillustrated as separate components, it will be appreciated that multiplecomponents can be implemented as a single component, or a singlecomponent can be implemented as multiple components, without departingfrom example embodiments.

Further, the various embodiments can be implemented as a method,apparatus or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device or computer-readable storage/communicationsmedia. For example, computer readable storage media can include, but arenot limited to, magnetic storage devices (e.g., hard disk, floppy disk,magnetic strips), optical disks (e.g., compact disk (CD), digitalversatile disk (DVD)), smart cards, and flash memory devices (e.g.,card, stick, key drive). Of course, those skilled in the art willrecognize many modifications can be made to this configuration withoutdeparting from the scope or spirit of the various embodiments.

In addition, the words “example” and “exemplary” are used herein to meanserving as an instance or illustration. Any embodiment or designdescribed herein as “example” or “exemplary” is not necessarily to beconstrued as preferred or advantageous over other embodiments ordesigns. Rather, use of the word example or exemplary is intended topresent concepts in a concrete fashion. As used in this application, theterm “or” is intended to mean an inclusive “or” rather than an exclusive“or”. That is, unless specified otherwise or clear from context, “Xemploys A or B” is intended to mean any of the natural inclusivepermutations. That is, if X employs A; X employs B; or X employs both Aand B, then “X employs A or B” is satisfied under any of the foregoinginstances. In addition, the articles “a” and “an” as used in thisapplication and the appended claims should generally be construed tomean “one or more” unless specified otherwise or clear from context tobe directed to a singular form.

Moreover, terms such as “user equipment,” “mobile station,” “mobile,”subscriber station,” “access terminal,” “terminal,” “handset,” “mobiledevice” (and/or terms representing similar terminology) can refer to awireless device utilized by a subscriber or user of a wirelesscommunication service to receive or convey data, control, voice, video,sound, gaming or substantially any data-stream or signaling-stream. Theforegoing terms are utilized interchangeably herein and with referenceto the related drawings.

Furthermore, the terms “user,” “subscriber,” “customer,” “consumer” andthe like are employed interchangeably throughout, unless contextwarrants particular distinctions among the terms. It should beappreciated that such terms can refer to human entities or automatedcomponents supported through artificial intelligence (e.g., a capacityto make inference based, at least, on complex mathematical formalisms),which can provide simulated vision, sound recognition and so forth.

As employed herein, the term “processor” can refer to substantially anycomputing processing unit or device comprising, but not limited tocomprising, single-core processors; single-processors with softwaremultithread execution capability; multi-core processors; multi-coreprocessors with software multithread execution capability; multi-coreprocessors with hardware multithread technology; parallel platforms; andparallel platforms with distributed shared memory. Additionally, aprocessor can refer to an integrated circuit, an application specificintegrated circuit (ASIC), a digital signal processor (DSP), a fieldprogrammable gate array (FPGA), a programmable logic controller (PLC), acomplex programmable logic device (CPLD), a discrete gate or transistorlogic, discrete hardware components or any combination thereof designedto perform the functions described herein. Processors can exploitnano-scale architectures such as, but not limited to, molecular andquantum-dot based transistors, switches and gates, in order to optimizespace usage or enhance performance of user equipment. A processor canalso be implemented as a combination of computing processing units.

As used herein, terms such as “data storage,” data storage,” “database,”and substantially any other information storage component relevant tooperation and functionality of a component, refer to “memorycomponents,” or entities embodied in a “memory” or components comprisingthe memory. It will be appreciated that the memory components orcomputer-readable storage media, described herein can be either volatilememory or nonvolatile memory or can include both volatile andnonvolatile memory.

What has been described above includes mere examples of variousembodiments. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing these examples, but one of ordinary skill in the art canrecognize that many further combinations and permutations of the presentembodiments are possible. Accordingly, the embodiments disclosed and/orclaimed herein are intended to embrace all such alterations,modifications and variations that fall within the spirit and scope ofthe appended claims. Furthermore, to the extent that the term “includes”is used in either the detailed description or the claims, such term isintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

As may also be used herein, the term(s) “operably coupled to”, “coupledto”, and/or “coupling” includes direct coupling between items and/orindirect coupling between items via one or more intervening items. Suchitems and intervening items include, but are not limited to, junctions,communication paths, components, circuit elements, circuits, functionalblocks, and/or devices. As an example of indirect coupling, a signalconveyed from a first item to a second item may be modified by one ormore intervening items by modifying the form, nature or format ofinformation in a signal, while one or more elements of the informationin the signal are nevertheless conveyed in a manner than can berecognized by the second item. In a further example of indirectcoupling, an action in a first item can cause a reaction on the seconditem, as a result of actions and/or reactions in one or more interveningitems.

Although specific embodiments have been illustrated and describedherein, it should be appreciated that any arrangement which achieves thesame or similar purpose may be substituted for the embodiments describedor shown by the subject disclosure. The subject disclosure is intendedto cover any and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, can be used in the subject disclosure.For instance, one or more features from one or more embodiments can becombined with one or more features of one or more other embodiments. Inone or more embodiments, features that are positively recited can alsobe negatively recited and excluded from the embodiment with or withoutreplacement by another structural and/or functional feature. The stepsor functions described with respect to the embodiments of the subjectdisclosure can be performed in any order. The steps or functionsdescribed with respect to the embodiments of the subject disclosure canbe performed alone or in combination with other steps or functions ofthe subject disclosure, as well as from other embodiments or from othersteps that have not been described in the subject disclosure. Further,more than or less than all of the features described with respect to anembodiment can also be utilized.

What is claimed is:
 1. A device, comprising: a processing systemincluding a processor; and a memory that stores executable instructionsthat, when executed by the processing system, facilitate performance ofoperations, the operations comprising: monitoring a plurality of datapackets in a data pipeline of a network, wherein the monitoring includesanalyzing header information associated with the plurality of datapackets to detect an indicator associated with a first data object type;identifying a first data object in a first set of data packets of theplurality of data packets according to the monitoring of the pluralityof data packets and according to a model relating to the first dataobject type, wherein the model is trained based on associations betweendata objects of the first data object type and analyzed headerinformation associated with other data packets; instantiating anintelligent router at the data pipeline of the network responsive to theidentifying the first data object, wherein the intelligent routerincludes a packet monitoring function; switching the first set of datapackets to the intelligent router to cause the intelligent router togenerate an extracted first data object from the first set of datapackets, responsive to the instantiating the intelligent router;transmitting the extracted first data object to a client device via thedata pipeline of the network responsive to a request from the clientdevice for the first data object; and decommissioning the intelligentrouter after the transmitting the extracted first data object.
 2. Thedevice of claim 1, wherein the model relating to the first data objecttype includes an association of the first data object type with asequence of header information, and wherein the model relating to thefirst data object type is trained via a machine learning algorithm. 3.The device of claim 1, wherein the monitoring the plurality of datapackets includes sampling one or more data packets of the plurality ofdata packets.
 4. The device of claim 1, wherein the indicator associatedwith the first data object type includes metadata.
 5. The device ofclaim 1, wherein the identifying the first data object comprisesidentifying, in header information associated with the first set of datapackets, metadata associated with the first data object type.
 6. Thedevice of claim 1, wherein the intelligent router further includes asolid-state memory device and a graphical processing unit.
 7. The deviceof claim 6, wherein the solid-state memory device stores the extractedfirst data object, organizes the extracted first data object, schedulesdelivery of the extracted first data object, or any combination thereof.8. The device of claim 6, wherein the graphical processing unit performsgraphical functions associated with the extracted first data object. 9.The device of claim 1, wherein the packet monitoring function isconfigured to analyze at least a portion of payload informationassociated with the first data object, identifies metadata informationassociated with the first data object, determines the first data objecttype associated with the first data object, or any combination thereof.10. The device of claim 1, wherein the transmitting the extracted firstdata object comprises transmitting the extracted first data object fromthe intelligent router to the client device.
 11. A method, comprising:monitoring, by a processing system including a processor, a plurality ofdata packets a network, wherein the monitoring includes analyzing headerinformation associated with the plurality of data packets to detect anindicator associated with a first data object type; detecting, by theprocessing system, a first data object in a first set of data packets ofthe plurality of data packets according to the monitoring of theplurality of data packets and according to a machine learning modelrelating to the first data object type, wherein the machine learningmodel is trained based on associations between data objects of the firstdata object type and analyzed header information of other data packets;instantiating, by the processing system, an intelligent router in thenetwork responsive to the detecting the first data object, wherein theintelligent router includes a packet monitoring function; providing, bythe processing system, the first set of data packets to the intelligentrouter to cause the intelligent router to generate an extracted firstdata object from the first set of data packets, responsive to theinstantiating the intelligent router; transmitting, by the processingsystem, the extracted first data object to a client device via thenetwork responsive to a request from the client device for the firstdata object; and decommissioning, by the processing system, theintelligent router after the transmitting the extracted first dataobject.
 12. The method of claim 11, wherein the machine learning modelrelating to the first data object type includes an association of thefirst data object type with a sequence of header information.
 13. Themethod of claim 11, wherein the intelligent router stores the extractedfirst data object as a cached first data object.
 14. The method of claim11, wherein the transmitting the extracted first data object comprisestransmitting the extracted first data object from the intelligent routerto the client device.
 15. The method of claim 11, wherein theintelligent router further includes a solid-state memory device and agraphical processing unit.
 16. A non-transitory machine-readable medium,comprising executable instructions that, when executed by a processingsystem including a processor, facilitate performance of operations, theoperations comprising: analyzing header information of a plurality ofdata packets in a data pipeline of a network to detect an indicatorassociated with a first data object type; identifying a first dataobject in a first set of data packets of the plurality of data packetsaccording to the analyzing of the header information and according to amodel, wherein the model is trained based on associations between dataobjects of the first data object type and analyzed header information ofother data packets; instantiating a router at the data pipeline of thenetwork responsive to the identifying the first data object, wherein therouter includes a packet monitoring function; switching the first set ofdata packets to the router to enable the router to derive an extractedfirst data object from the first set of data packets after theinstantiating the router; causing the extracted first data object to beprovided to a client device responsive to receiving a request from theclient device for the first data object; and decommissioning the routerresponsive to the causing the extracted first data object to be providedto the client device.
 17. The non-transitory machine-readable medium ofclaim 16, wherein the model includes an association of the first dataobject type with a sequence of header information, and wherein the modelis trained via a machine learning algorithm.
 18. The non-transitorymachine-readable medium of claim 16, wherein the router stores theextracted first data object as a cached first data object.
 19. Thenon-transitory machine-readable medium of claim 16, wherein the causingthe extracted first data object to be provided to the client devicecomprises causing the extracted first data object to be provided fromthe router to the client device.
 20. The non-transitory machine-readablemedium of claim 16, wherein the router further includes a solid-statememory device and a graphical processing unit.